Data-driven Radar Processing Using a Parametric Convolutional Neural Network for Human Activity Classification - Stadelmayer, Thomas; Santra, Avik; Weigel, Robert; Lurz, Fabian (IEEE Sensors Journal, 2021)
The paper proposes a data-driven pre-processing optimization for radar data using a parametric convolutional neural network. The proposed method is applied on human activity classification as a use case. Present radar-based activity recognition system exploit micro-Doppler signature by generating Doppler spectrograms or a temporal series of range-Doppler maps, followed by deep neural networks or machine learning approaches for classification. Those radar data representations are typically generated on the basis of short-time Fourier transformations. A Fourier transformation equally resolves the frequency space, which may be sub-optimal in some applications. Although deep convolutional neural networks (DCNN) have been shown to implicitly learn features from raw sensor data in other fields, such as speech recognition, yet, for the case of radar-based DCNNs, pre-processing is required to develop a scalable and robust classification or regression application. In this paper, we propose a parametric convolutional neural network that mimics the radar pre-processing across fast-time and slow-time radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for classification of various human activities. During training only the filter parameters of the 2D sinc filters or 2D wavelets are learned, leading to optimized feature representation for the classification task. It is demonstrated that our proposed solution shows improved results compared to equivalent DCNN architectures that rely on Doppler spectrograms or radar data cubes as input data.
CRIS-Team Scopus Importer:2021-08-27
Keywords: 2D Sinc Filters; 2D Wavelets; Chirp; Deep Learning; Doppler effect; Doppler radar; Feature extraction; Human Activity Classification; Parametric CNN; Radar; Sensors; Spectrogram

Human Activity Classification Using mm-Wave FMCW Radar by Improved Representation Learning - Stadelmayer, Thomas; Stadelmayer, Markus; Santra, Avik; Weigel, Robert; Lurz, Fabian (Proceedings of the 4th ACM Workshop on Millimeter-Wave Networks and Sensing Systems, 2020)
The paper proposes a novel Euclidean distance softmax layer for radar-based human activity classification. The method aims to overcome the angular dependency of classical softmax approaches. Through the freedoms thus gained, the activity classes can be distributed freely within the entire embedded feature space, due to which the dimension of the embeddings and the whole neural network size can be reduced. The performance of our novel deep learning architecture is evaluated for 60 GHz mm-wave radar sensor-based human activity classification. The results show that the proposed approach increases the robustness against random and unknown movements compared to state-of-art representation learning technique

Improved Indoor Semi-Static Human Target Detection and Localization using FMCW Radar - Kaiser, Kevin; Stadelmayer, Thomas; Felgentreff, Jens; Weigel, Robert; Lurz, Fabian; Santra, Avik (20th European Radar Conference, EuRAD 2023, 2023)
This paper presents a novel processing approach for semi-static human indoor perception and localization with Frequency modulated continuous wave (FMCW) radar. When analyzing semi-static targets with FMCW radar, minimal phase variations of standing or sitting human targets in slow-time can result in missed detections under traditional processing schemes. The common approach of studying micro-Doppler signatures over an extended period of time can, however, be costly in terms of both memory and compute resources. In this work, we introduce an approach with low memory and computational footprint, which relies on signatures beyond micro-Doppler for a novel presence score. We show on real-world recordings how this maximal variance based presence score can bring improvement in detection of static humans. To further increase detections in the range angle space, we introduce a novel angle of arrival estimation by creating a synthetic spatial signal from all phase differences between antennas over time. We show a 22.9% gain in human detection performance on a large dataset of humans in semi-static applications and highlight the reduction in computational complexity of both of these concepts.
CRIS-Team Scopus Importer:2023-12-01
Keywords: ceramics; coaxial resonators; delay filters; power amplifiers

Improved Target Detection and Feature Extraction using a Complex-Valued Adaptive Sinc Filter on Radar Time Domain Data - Stadelmayer, Thomas; Santra, Avik; Stadelmayer, Markus; Weigel, Robert; Lurz, Fabian (29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021), 2021)
In state-of-art radar classification tasks the raw time domain ADC data is transformed to frequency domain wherein the target is detected. Feature data, such as the micro-Doppler signature, is extracted for classification of e.g. a performed gesture or a person based on its gait. However, the transformation into frequency domain using short-time Fourier transform resulting in erroneous target detection due to superposition of target components thus leading to sub-optimal feature for subsequent classification task. Thus, in this paper we propose a target feature extraction approach that operates directly on 2D time domain radar data by using a complex-valued adaptive 2D sine filter. The proposed approach tracks the target's slow-time and fast-time frequencies by a regulation loop, which progressively adjusts the filter's center frequency to the target location. We demonstrate that the proposed approach extracts better features in both single- and multi-target scenarios, leading to improved classification accuracy in gait classification problems using radars. Furthermore, the proposed time domain feature extraction approach facilitates the use of a parametric neural network that works directly on time domain data.<br
CRIS-Team WoS Importer:2022-04-22
Keywords: adaptive sinc filter; feature extraction; radar classification; time domain processing

Improved Target Detection and Feature Extraction using a Complex-Valued Adaptive Sinc Filter on Radar Time Domain Data - Stadelmayer, Thomas; Santra, Avik; Stadelmayer, Markus; Weigel, Robert; Lurz, Fabian (29th European Signal Processing Conference, 2021)
In state-of-art radar classification tasks the raw time domain ADC data is transformed to frequency domain wherein the target is detected. Feature data, such as the micro-Doppler signature, is extracted for classification of e.g. a performed gesture or a person based on its gait. However, the transformation into frequency domain using short-time Fourier transform resulting in erroneous target detection due to superposition of target components thus leading to sub-optimal feature for subsequent classification task. Thus, in this paper we propose a target feature extraction approach that operates directly on 2D time domain radar data by using a complex-valued adaptive 2D sine filter. The proposed approach tracks the target's slow-time and fast-time frequencies by a regulation loop, which progressively adjusts the filter's center frequency to the target location. We demonstrate that the proposed approach extracts better features in both single- and multi-target scenarios, leading to improved classification accuracy in gait classification problems using radars. Furthermore, the proposed time domain feature extraction approach facilitates the use of a parametric neural network that works directly on time domain dat

LABEL-AWARE RANKED LOSS FOR ROBUST PEOPLE COUNTING USING AUTOMOTIVE IN-CABIN RADAR - Servadei, Lorenzo; Sun, Huawei; Ott, Julius; Stephan, Michael; Hazra, Souvik; Stadelmayer, Thomas; Lopera, Daniela Sanchéz; Wille, Robert; Santra, Avik (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2022)
In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function. Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage of the ranked ordering of the labels in regression problems. To this end, we first show that the loss minimises when datapoints of different labels are ranked and laid at uniform angles between each other in the embedding space. Then, to measure its performance, we apply the proposed loss on a regression task of people counting with a short-range radar in a challenging scenario, namely a vehicle cabin. The introduced approach improves the accuracy as well as the neighboring labels accuracy up to 83.0% and 99.9%: An increase of 6.7% and 2.1% on state-of-the-art methods, respectively.
Created from Fastlane, Scopus look-up
Keywords: Deep Metric Learning; People Counting; Radar Signal Processing

Out-of-Distribution Detection for Radar-based Gesture Recognition Using Metric-Learning - Stadelmayer, Thomas; Servadei, Lorenzo; Santra, Avik; Weigel, Robert; Lurz, Fabian (IEEE Radio and Wireless Symposium, RWS, 2023)
The paper addresses the question how and to what extent metric learning can be beneficial for reducing the false alarm rate in radar-based hand gesture recognition systems. To this end, we evaluate different metric learning approaches for out-of-distribution or unknown motion detection. We found that metric learning can help to significantly increase the out-of-distribution capabilities of the network. We further investigated what conditions must be met for metric learning to work well, and found that the composition of the data set for known gestures has a large influence on the out-of-distribution detection rate.
CRIS-Team Scopus Importer:2023-03-17
Keywords: FMCW radar; hand gesture recognition; metric learning; out-of-distribution detection

Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation - Stephan, Michael; Stadelmayer, Thomas; Santra, Avik; Fischer, Georg; Weigel, Robert; Lurz, Fabian (25th International Conference on Pattern Recognition, 2021)